Overview

Dataset statistics

Number of variables16
Number of observations27
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.5 KiB
Average record size in memory132.7 B

Variable types

Categorical2
Numeric14

Alerts

Casos_entre_nascidos is highly overall correlated with Espinha bífidaprop and 10 other fieldsHigh correlation
Espinha bífidaprop is highly overall correlated with Casos_entre_nascidos and 4 other fieldsHigh correlation
Outras malformações congênitas do sistema nervosoprop is highly overall correlated with UF and 1 other fieldsHigh correlation
Malformações congênitas do aparelho circulatórioprop is highly overall correlated with Casos_entre_nascidos and 7 other fieldsHigh correlation
Fenda labial e fenda palatinaprop is highly overall correlated with Casos_entre_nascidos and 6 other fieldsHigh correlation
Ausência atresia e estenose do intestino delgadoprop is highly overall correlated with Malformações congênitas do aparelho circulatórioprop and 2 other fieldsHigh correlation
Outras malformações congênitas do aparelho digestivoprop is highly overall correlated with Casos_entre_nascidos and 2 other fieldsHigh correlation
Testiculo não-descidoprop is highly overall correlated with Fenda labial e fenda palatinaprop and 5 other fieldsHigh correlation
Outras malformações do aparelho geniturinárioprop is highly overall correlated with Casos_entre_nascidos and 9 other fieldsHigh correlation
Deformidades congênitas do quadrilprop is highly overall correlated with UF and 1 other fieldsHigh correlation
Deformidades congênitas dos pésprop is highly overall correlated with Casos_entre_nascidos and 6 other fieldsHigh correlation
Outras malformações e deformidades congênitas do aparelho osteomuscularprop is highly overall correlated with Casos_entre_nascidos and 8 other fieldsHigh correlation
Outras malformações congênitasprop is highly overall correlated with Casos_entre_nascidos and 4 other fieldsHigh correlation
Anomalias cromossômicas não classificadas em outra parteprop is highly overall correlated with Casos_entre_nascidos and 3 other fieldsHigh correlation
UF is highly overall correlated with Casos_entre_nascidos and 14 other fieldsHigh correlation
Estado is highly overall correlated with Casos_entre_nascidos and 14 other fieldsHigh correlation
UF is uniformly distributedUniform
Estado is uniformly distributedUniform
UF has unique valuesUnique
Estado has unique valuesUnique
Casos_entre_nascidos has unique valuesUnique
Espinha bífidaprop has unique valuesUnique
Outras malformações congênitas do sistema nervosoprop has unique valuesUnique
Malformações congênitas do aparelho circulatórioprop has unique valuesUnique
Fenda labial e fenda palatinaprop has unique valuesUnique
Outras malformações congênitas do aparelho digestivoprop has unique valuesUnique
Outras malformações do aparelho geniturinárioprop has unique valuesUnique
Deformidades congênitas dos pésprop has unique valuesUnique
Outras malformações e deformidades congênitas do aparelho osteomuscularprop has unique valuesUnique
Outras malformações congênitasprop has unique valuesUnique
Anomalias cromossômicas não classificadas em outra parteprop has unique valuesUnique
Ausência atresia e estenose do intestino delgadoprop has 7 (25.9%) zerosZeros
Testiculo não-descidoprop has 3 (11.1%) zerosZeros
Deformidades congênitas do quadrilprop has 6 (22.2%) zerosZeros

Reproduction

Analysis started2023-04-16 17:09:43.116587
Analysis finished2023-04-16 17:10:02.526325
Duration19.41 seconds
Software versionydata-profiling vv4.1.2
Download configurationconfig.json

Variables

UF
Categorical

HIGH CORRELATION  UNIFORM  UNIQUE 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size344.0 B
AC
 
1
PB
 
1
SP
 
1
SE
 
1
SC
 
1
Other values (22)
22 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters54
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique27 ?
Unique (%)100.0%

Sample

1st rowAC
2nd rowAL
3rd rowAM
4th rowAP
5th rowBA

Common Values

ValueCountFrequency (%)
AC 1
 
3.7%
PB 1
 
3.7%
SP 1
 
3.7%
SE 1
 
3.7%
SC 1
 
3.7%
RS 1
 
3.7%
RR 1
 
3.7%
RO 1
 
3.7%
RN 1
 
3.7%
RJ 1
 
3.7%
Other values (17) 17
63.0%

Length

2023-04-16T14:10:02.593465image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ac 1
 
3.7%
al 1
 
3.7%
am 1
 
3.7%
ap 1
 
3.7%
ba 1
 
3.7%
ce 1
 
3.7%
df 1
 
3.7%
es 1
 
3.7%
go 1
 
3.7%
ma 1
 
3.7%
Other values (17) 17
63.0%

Most occurring characters

ValueCountFrequency (%)
A 7
13.0%
P 7
13.0%
R 7
13.0%
S 6
11.1%
M 5
9.3%
E 4
7.4%
O 3
 
5.6%
C 3
 
5.6%
B 2
 
3.7%
G 2
 
3.7%
Other values (7) 8
14.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 54
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 7
13.0%
P 7
13.0%
R 7
13.0%
S 6
11.1%
M 5
9.3%
E 4
7.4%
O 3
 
5.6%
C 3
 
5.6%
B 2
 
3.7%
G 2
 
3.7%
Other values (7) 8
14.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 54
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 7
13.0%
P 7
13.0%
R 7
13.0%
S 6
11.1%
M 5
9.3%
E 4
7.4%
O 3
 
5.6%
C 3
 
5.6%
B 2
 
3.7%
G 2
 
3.7%
Other values (7) 8
14.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 54
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 7
13.0%
P 7
13.0%
R 7
13.0%
S 6
11.1%
M 5
9.3%
E 4
7.4%
O 3
 
5.6%
C 3
 
5.6%
B 2
 
3.7%
G 2
 
3.7%
Other values (7) 8
14.8%

Estado
Categorical

HIGH CORRELATION  UNIFORM  UNIQUE 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size344.0 B
Acre
 
1
Paraíba
 
1
São Paulo
 
1
Sergipe
 
1
Santa Catarina
 
1
Other values (22)
22 

Length

Max length19
Median length16
Mean length9.4074074
Min length4

Characters and Unicode

Total characters254
Distinct characters37
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique27 ?
Unique (%)100.0%

Sample

1st rowAcre
2nd rowAlagoas
3rd rowAmazonas
4th rowAmapá
5th rowBahia

Common Values

ValueCountFrequency (%)
Acre 1
 
3.7%
Paraíba 1
 
3.7%
São Paulo 1
 
3.7%
Sergipe 1
 
3.7%
Santa Catarina 1
 
3.7%
Rio Grande do Sul 1
 
3.7%
Roraima 1
 
3.7%
Rondônia 1
 
3.7%
Rio Grande do Norte 1
 
3.7%
Rio de Janeiro 1
 
3.7%
Other values (17) 17
63.0%

Length

2023-04-16T14:10:02.682243image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
rio 3
 
6.8%
do 3
 
6.8%
sul 2
 
4.5%
grosso 2
 
4.5%
grande 2
 
4.5%
mato 2
 
4.5%
federal 1
 
2.3%
ceará 1
 
2.3%
bahia 1
 
2.3%
amapá 1
 
2.3%
Other values (26) 26
59.1%

Most occurring characters

ValueCountFrequency (%)
a 37
14.6%
o 27
 
10.6%
r 20
 
7.9%
i 17
 
6.7%
17
 
6.7%
n 15
 
5.9%
e 13
 
5.1%
s 12
 
4.7%
t 10
 
3.9%
d 8
 
3.1%
Other values (27) 78
30.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 197
77.6%
Uppercase Letter 40
 
15.7%
Space Separator 17
 
6.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 37
18.8%
o 27
13.7%
r 20
10.2%
i 17
8.6%
n 15
7.6%
e 13
 
6.6%
s 12
 
6.1%
t 10
 
5.1%
d 8
 
4.1%
u 5
 
2.5%
Other values (12) 33
16.8%
Uppercase Letter
ValueCountFrequency (%)
G 6
15.0%
S 6
15.0%
P 6
15.0%
R 5
12.5%
M 4
10.0%
A 4
10.0%
C 2
 
5.0%
J 1
 
2.5%
N 1
 
2.5%
E 1
 
2.5%
Other values (4) 4
10.0%
Space Separator
ValueCountFrequency (%)
17
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 237
93.3%
Common 17
 
6.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 37
15.6%
o 27
 
11.4%
r 20
 
8.4%
i 17
 
7.2%
n 15
 
6.3%
e 13
 
5.5%
s 12
 
5.1%
t 10
 
4.2%
d 8
 
3.4%
G 6
 
2.5%
Other values (26) 72
30.4%
Common
ValueCountFrequency (%)
17
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 243
95.7%
None 11
 
4.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 37
15.2%
o 27
11.1%
r 20
 
8.2%
i 17
 
7.0%
17
 
7.0%
n 15
 
6.2%
e 13
 
5.3%
s 12
 
4.9%
t 10
 
4.1%
d 8
 
3.3%
Other values (23) 67
27.6%
None
ValueCountFrequency (%)
á 5
45.5%
í 3
27.3%
ã 2
 
18.2%
ô 1
 
9.1%

Casos_entre_nascidos
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0080427911
Minimum0.0047129805
Maximum0.017755549
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size344.0 B
2023-04-16T14:10:02.778458image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.0047129805
5-th percentile0.0048927887
Q10.0065581565
median0.0075002612
Q30.0087659216
95-th percentile0.012405424
Maximum0.017755549
Range0.013042568
Interquartile range (IQR)0.0022077651

Descriptive statistics

Standard deviation0.0027436813
Coefficient of variation (CV)0.34113547
Kurtosis5.1899039
Mean0.0080427911
Median Absolute Deviation (MAD)0.001111231
Skewness1.9110409
Sum0.21715536
Variance7.5277869 × 10-6
MonotonicityNot monotonic
2023-04-16T14:10:02.850680image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
0.006459705237 1
 
3.7%
0.00625389755 1
 
3.7%
0.01295359725 1
 
3.7%
0.01112635269 1
 
3.7%
0.008920351085 1
 
3.7%
0.009693721286 1
 
3.7%
0.006987916727 1
 
3.7%
0.007096899981 1
 
3.7%
0.007848652572 1
 
3.7%
0.007062984136 1
 
3.7%
Other values (17) 17
63.0%
ValueCountFrequency (%)
0.004712980454 1
3.7%
0.00474205107 1
3.7%
0.005244509928 1
3.7%
0.005396987763 1
3.7%
0.005945681429 1
3.7%
0.00625389755 1
3.7%
0.006459705237 1
3.7%
0.006656607793 1
3.7%
0.006933363572 1
3.7%
0.006987916727 1
3.7%
ValueCountFrequency (%)
0.01775554861 1
3.7%
0.01295359725 1
3.7%
0.01112635269 1
3.7%
0.0102304518 1
3.7%
0.01020940801 1
3.7%
0.009693721286 1
3.7%
0.008920351085 1
3.7%
0.008611492151 1
3.7%
0.008463310053 1
3.7%
0.007848652572 1
3.7%

Espinha bífidaprop
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.022706606
Minimum0.010656295
Maximum0.041193102
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size344.0 B
2023-04-16T14:10:02.950577image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.010656295
5-th percentile0.012326674
Q10.015908404
median0.019116412
Q30.029891335
95-th percentile0.039437861
Maximum0.041193102
Range0.030536807
Interquartile range (IQR)0.013982931

Descriptive statistics

Standard deviation0.0092280401
Coefficient of variation (CV)0.40640333
Kurtosis-0.76370559
Mean0.022706606
Median Absolute Deviation (MAD)0.00497858
Skewness0.66995597
Sum0.61307835
Variance8.5156724 × 10-5
MonotonicityNot monotonic
2023-04-16T14:10:03.038892image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
0.01254311696 1
 
3.7%
0.01425389755 1
 
3.7%
0.02981369153 1
 
3.7%
0.03962810547 1
 
3.7%
0.02557440105 1
 
3.7%
0.02297090352 1
 
3.7%
0.01455815985 1
 
3.7%
0.03899395594 1
 
3.7%
0.02268396697 1
 
3.7%
0.02310791396 1
 
3.7%
Other values (17) 17
63.0%
ValueCountFrequency (%)
0.01065629454 1
3.7%
0.01223391241 1
3.7%
0.01254311696 1
3.7%
0.01289608443 1
3.7%
0.01425389755 1
3.7%
0.01455815985 1
3.7%
0.01578615028 1
3.7%
0.01603065672 1
3.7%
0.01663374571 1
3.7%
0.01671367387 1
3.7%
ValueCountFrequency (%)
0.04119310203 1
3.7%
0.03962810547 1
3.7%
0.03899395594 1
3.7%
0.03527741999 1
3.7%
0.03300786048 1
3.7%
0.03239673541 1
3.7%
0.02996897806 1
3.7%
0.02981369153 1
3.7%
0.02557440105 1
3.7%
0.0240949921 1
3.7%

Outras malformações congênitas do sistema nervosoprop
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.065277016
Minimum0.025086234
Maximum0.10672758
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size344.0 B
2023-04-16T14:10:03.122301image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.025086234
5-th percentile0.042911408
Q10.054101866
median0.063582832
Q30.070863267
95-th percentile0.095579591
Maximum0.10672758
Range0.081641349
Interquartile range (IQR)0.016761401

Descriptive statistics

Standard deviation0.01862672
Coefficient of variation (CV)0.28534883
Kurtosis0.22389045
Mean0.065277016
Median Absolute Deviation (MAD)0.0092083142
Skewness0.41678071
Sum1.7624794
Variance0.0003469547
MonotonicityNot monotonic
2023-04-16T14:10:03.200527image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
0.02508623393 1
 
3.7%
0.06414253898 1
 
3.7%
0.08293626917 1
 
3.7%
0.06401463192 1
 
3.7%
0.06240153856 1
 
3.7%
0.04287901991 1
 
3.7%
0.09462803902 1
 
3.7%
0.05849093391 1
 
3.7%
0.08846747119 1
 
3.7%
0.06480262827 1
 
3.7%
Other values (17) 17
63.0%
ValueCountFrequency (%)
0.02508623393 1
3.7%
0.04287901991 1
3.7%
0.04298698109 1
3.7%
0.04762993446 1
3.7%
0.05112474438 1
3.7%
0.05194943588 1
3.7%
0.05382921458 1
3.7%
0.05437451765 1
3.7%
0.05590038166 1
3.7%
0.05849093391 1
3.7%
ValueCountFrequency (%)
0.1067275825 1
3.7%
0.09598739858 1
3.7%
0.09462803902 1
3.7%
0.09377930603 1
3.7%
0.08846747119 1
3.7%
0.08293626917 1
3.7%
0.07103311397 1
3.7%
0.07069341927 1
3.7%
0.06977758395 1
3.7%
0.06480262827 1
3.7%

Malformações congênitas do aparelho circulatórioprop
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.061124497
Minimum0.0062715585
Maximum0.29849829
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size344.0 B
2023-04-16T14:10:03.299278image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.0062715585
5-th percentile0.013668114
Q10.030176434
median0.045289855
Q30.065898127
95-th percentile0.12901273
Maximum0.29849829
Range0.29222673
Interquartile range (IQR)0.035721693

Descriptive statistics

Standard deviation0.05665762
Coefficient of variation (CV)0.92692166
Kurtosis11.869025
Mean0.061124497
Median Absolute Deviation (MAD)0.01678206
Skewness3.0603394
Sum1.6503614
Variance0.0032100859
MonotonicityNot monotonic
2023-04-16T14:10:03.383276image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
0.006271558482 1
 
3.7%
0.0285077951 1
 
3.7%
0.2984982934 1
 
3.7%
0.03962810547 1
 
3.7%
0.1155962927 1
 
3.7%
0.134762634 1
 
3.7%
0.0291163197 1
 
3.7%
0.03899395594 1
 
3.7%
0.06578350422 1
 
3.7%
0.06028151467 1
 
3.7%
Other values (17) 17
63.0%
ValueCountFrequency (%)
0.006271558482 1
3.7%
0.01250390747 1
3.7%
0.01638459463 1
3.7%
0.02664073635 1
3.7%
0.0285077951 1
3.7%
0.0291163197 1
3.7%
0.02981828387 1
3.7%
0.03053458423 1
3.7%
0.03118827321 1
3.7%
0.03180817225 1
3.7%
ValueCountFrequency (%)
0.2984982934 1
3.7%
0.134762634 1
3.7%
0.1155962927 1
3.7%
0.09923792761 1
3.7%
0.09148568659 1
3.7%
0.07711808091 1
3.7%
0.06601274897 1
3.7%
0.06578350422 1
3.7%
0.06390593047 1
3.7%
0.06028151467 1
3.7%

Fenda labial e fenda palatinaprop
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.062727505
Minimum0.036624388
Maximum0.086523737
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size344.0 B
2023-04-16T14:10:03.471969image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.036624388
5-th percentile0.043352797
Q10.054406198
median0.063171194
Q30.070232294
95-th percentile0.082179361
Maximum0.086523737
Range0.049899349
Interquartile range (IQR)0.015826096

Descriptive statistics

Standard deviation0.012626823
Coefficient of variation (CV)0.20129643
Kurtosis-0.39965131
Mean0.062727505
Median Absolute Deviation (MAD)0.0089723069
Skewness0.047795319
Sum1.6936426
Variance0.00015943666
MonotonicityNot monotonic
2023-04-16T14:10:03.550653image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
0.05644402634 1
 
3.7%
0.06414253898 1
 
3.7%
0.07317906103 1
 
3.7%
0.08230452675 1
 
3.7%
0.0757002271 1
 
3.7%
0.0865237366 1
 
3.7%
0.05095355947 1
 
3.7%
0.08188730747 1
 
3.7%
0.06124671082 1
 
3.7%
0.04420644409 1
 
3.7%
Other values (17) 17
63.0%
ValueCountFrequency (%)
0.03662438799 1
3.7%
0.04298694809 1
3.7%
0.04420644409 1
3.7%
0.05095355947 1
3.7%
0.05262050095 1
3.7%
0.05345817708 1
3.7%
0.05419888701 1
3.7%
0.05461350952 1
3.7%
0.05626758362 1
3.7%
0.05640864933 1
3.7%
ValueCountFrequency (%)
0.0865237366 1
3.7%
0.08230452675 1
3.7%
0.08188730747 1
3.7%
0.08094751193 1
3.7%
0.0757002271 1
3.7%
0.0752272169 1
3.7%
0.07317906103 1
3.7%
0.06728552738 1
3.7%
0.06622471342 1
3.7%
0.06606312699 1
3.7%

Ausência atresia e estenose do intestino delgadoprop
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct21
Distinct (%)77.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0020477943
Minimum0
Maximum0.0075889397
Zeros7
Zeros (%)25.9%
Negative0
Negative (%)0.0%
Memory size344.0 B
2023-04-16T14:10:03.636105image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.00038381233
median0.0019051974
Q30.002843336
95-th percentile0.0056682095
Maximum0.0075889397
Range0.0075889397
Interquartile range (IQR)0.0024595237

Descriptive statistics

Standard deviation0.001939155
Coefficient of variation (CV)0.94694816
Kurtosis1.5695804
Mean0.0020477943
Median Absolute Deviation (MAD)0.0013346921
Skewness1.1938126
Sum0.055290445
Variance3.7603222 × 10-6
MonotonicityNot monotonic
2023-04-16T14:10:03.700641image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
0 7
25.9%
0.006271558482 1
 
3.7%
0.002446782481 1
 
3.7%
0.007588939663 1
 
3.7%
0.004091904168 1
 
3.7%
0.002297090352 1
 
3.7%
0.002009383822 1
 
3.7%
0.003413645022 1
 
3.7%
0.002089209234 1
 
3.7%
0.0007676246622 1
 
3.7%
Other values (11) 11
40.7%
ValueCountFrequency (%)
0 7
25.9%
0.0007676246622 1
 
3.7%
0.0009637996839 1
 
3.7%
0.001246028285 1
 
3.7%
0.001332036817 1
 
3.7%
0.001612010553 1
 
3.7%
0.001640810232 1
 
3.7%
0.001905197378 1
 
3.7%
0.002009383822 1
 
3.7%
0.002079218214 1
 
3.7%
ValueCountFrequency (%)
0.007588939663 1
3.7%
0.006271558482 1
3.7%
0.004260395365 1
3.7%
0.004091904168 1
3.7%
0.003744827457 1
3.7%
0.003413645022 1
3.7%
0.00323988952 1
3.7%
0.002446782481 1
3.7%
0.002297090352 1
3.7%
0.002290093818 1
3.7%

Outras malformações congênitas do aparelho digestivoprop
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.083969317
Minimum0.01455816
Maximum1.1503595
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size344.0 B
2023-04-16T14:10:03.800508image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.01455816
5-th percentile0.025698368
Q10.032290081
median0.039505146
Q30.047858102
95-th percentile0.10326516
Maximum1.1503595
Range1.1358013
Interquartile range (IQR)0.015568022

Descriptive statistics

Standard deviation0.21396151
Coefficient of variation (CV)2.5480916
Kurtosis26.526384
Mean0.083969317
Median Absolute Deviation (MAD)0.0081311846
Skewness5.1320375
Sum2.2671716
Variance0.045779528
MonotonicityNot monotonic
2023-04-16T14:10:03.883968image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
0.02508623393 1
 
3.7%
0.03741648107 1
 
3.7%
0.1194354552 1
 
3.7%
0.06401463192 1
 
3.7%
0.04807987397 1
 
3.7%
0.04747320061 1
 
3.7%
0.01455815985 1
 
3.7%
0.05069214272 1
 
3.7%
0.04763633064 1
 
3.7%
0.0271266816 1
 
3.7%
Other values (17) 17
63.0%
ValueCountFrequency (%)
0.01455815985 1
3.7%
0.02508623393 1
3.7%
0.0271266816 1
3.7%
0.02891399052 1
3.7%
0.02982276755 1
3.7%
0.0306368468 1
3.7%
0.03157230057 1
3.7%
0.03300786048 1
3.7%
0.03550190823 1
3.7%
0.03738084855 1
3.7%
ValueCountFrequency (%)
1.150359487 1
3.7%
0.1194354552 1
3.7%
0.0655344805 1
3.7%
0.06401463192 1
3.7%
0.0533455266 1
3.7%
0.05069214272 1
3.7%
0.04807987397 1
3.7%
0.04763633064 1
3.7%
0.04747320061 1
3.7%
0.04512228138 1
3.7%

Testiculo não-descidoprop
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct25
Distinct (%)92.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.016282158
Minimum0
Maximum0.045724737
Zeros3
Zeros (%)11.1%
Negative0
Negative (%)0.0%
Memory size344.0 B
2023-04-16T14:10:03.971026image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.0094609282
median0.012543117
Q30.021702599
95-th percentile0.032967496
Maximum0.045724737
Range0.045724737
Interquartile range (IQR)0.012241671

Descriptive statistics

Standard deviation0.011288281
Coefficient of variation (CV)0.6932915
Kurtosis0.31040148
Mean0.016282158
Median Absolute Deviation (MAD)0.0072810669
Skewness0.69619603
Sum0.43961825
Variance0.0001274253
MonotonicityNot monotonic
2023-04-16T14:10:04.047098image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
0 3
 
11.1%
0.01254311696 1
 
3.7%
0.03028953229 1
 
3.7%
0.03360816136 1
 
3.7%
0.04572473708 1
 
3.7%
0.02455142501 1
 
3.7%
0.01990811639 1
 
3.7%
0.01949697797 1
 
3.7%
0.006805190092 1
 
3.7%
0.02210322205 1
 
3.7%
Other values (15) 15
55.6%
ValueCountFrequency (%)
0 3
11.1%
0.005262050095 1
 
3.7%
0.006805190092 1
 
3.7%
0.008397010664 1
 
3.7%
0.009134726469 1
 
3.7%
0.009787129924 1
 
3.7%
0.01024093507 1
 
3.7%
0.01039609107 1
 
3.7%
0.01044604617 1
 
3.7%
0.01123448237 1
 
3.7%
ValueCountFrequency (%)
0.04572473708 1
3.7%
0.03360816136 1
3.7%
0.03147261115 1
3.7%
0.03117539441 1
3.7%
0.03028953229 1
3.7%
0.02455142501 1
3.7%
0.02210322205 1
3.7%
0.02130197682 1
3.7%
0.01993645256 1
3.7%
0.01990811639 1
3.7%

Outras malformações do aparelho geniturinárioprop
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.074802812
Minimum0.021312589
Maximum0.14891918
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size344.0 B
2023-04-16T14:10:04.129115image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.021312589
5-th percentile0.036115107
Q10.047987172
median0.074152485
Q30.085588967
95-th percentile0.13953525
Maximum0.14891918
Range0.1276066
Interquartile range (IQR)0.037601795

Descriptive statistics

Standard deviation0.032614127
Coefficient of variation (CV)0.43600135
Kurtosis0.12936422
Mean0.074802812
Median Absolute Deviation (MAD)0.023980018
Skewness0.66634397
Sum2.0196759
Variance0.0010636813
MonotonicityNot monotonic
2023-04-16T14:10:04.200553image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
0.05017246786 1
 
3.7%
0.06770601336 1
 
3.7%
0.1308188647 1
 
3.7%
0.1432708429 1
 
3.7%
0.08592998752 1
 
3.7%
0.1056661562 1
 
3.7%
0.05823263939 1
 
3.7%
0.07798791187 1
 
3.7%
0.1066146448 1
 
3.7%
0.06831904996 1
 
3.7%
Other values (17) 17
63.0%
ValueCountFrequency (%)
0.02131258908 1
3.7%
0.03551655698 1
3.7%
0.03751172241 1
3.7%
0.0391485197 1
3.7%
0.03951578704 1
3.7%
0.04560443415 1
3.7%
0.04580187635 1
3.7%
0.05017246786 1
3.7%
0.05823263939 1
3.7%
0.0621283394 1
3.7%
ValueCountFrequency (%)
0.1489191845 1
3.7%
0.1432708429 1
3.7%
0.1308188647 1
3.7%
0.1066146448 1
3.7%
0.1056661562 1
3.7%
0.09923792761 1
3.7%
0.08592998752 1
3.7%
0.08524794677 1
3.7%
0.08520790729 1
3.7%
0.08286091672 1
3.7%

Deformidades congênitas do quadrilprop
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct22
Distinct (%)81.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0042719397
Minimum0
Maximum0.017041581
Zeros6
Zeros (%)22.2%
Negative0
Negative (%)0.0%
Memory size344.0 B
2023-04-16T14:10:04.398097image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.00076549463
median0.0024299171
Q30.0064004478
95-th percentile0.015017504
Maximum0.017041581
Range0.017041581
Interquartile range (IQR)0.0056349532

Descriptive statistics

Standard deviation0.0048281801
Coefficient of variation (CV)1.1302079
Kurtosis1.4624179
Mean0.0042719397
Median Absolute Deviation (MAD)0.0024299171
Skewness1.4197022
Sum0.11534237
Variance2.3311323 × 10-5
MonotonicityNot monotonic
2023-04-16T14:10:04.469213image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
0 6
22.2%
0.006237654642 1
 
3.7%
0.01608132452 1
 
3.7%
0.001022976042 1
 
3.7%
0.003062787136 1
 
3.7%
0.007279079924 1
 
3.7%
0.002268396697 1
 
3.7%
0.001507037867 1
 
3.7%
0.01253525541 1
 
3.7%
0.0007676246622 1
 
3.7%
Other values (12) 12
44.4%
ValueCountFrequency (%)
0 6
22.2%
0.0007633646058 1
 
3.7%
0.0007676246622 1
 
3.7%
0.001022976042 1
 
3.7%
0.001246028285 1
 
3.7%
0.001507037867 1
 
3.7%
0.001872413729 1
 
3.7%
0.002268396697 1
 
3.7%
0.00242991714 1
 
3.7%
0.002891399052 1
 
3.7%
ValueCountFrequency (%)
0.01704158146 1
3.7%
0.01608132452 1
3.7%
0.01253525541 1
3.7%
0.009525986892 1
3.7%
0.007340347443 1
3.7%
0.007279079924 1
3.7%
0.006563240928 1
3.7%
0.006237654642 1
3.7%
0.005345211581 1
3.7%
0.005262050095 1
3.7%

Deformidades congênitas dos pésprop
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.11302089
Minimum0.050953559
Maximum0.21338211
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size344.0 B
2023-04-16T14:10:04.550441image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.050953559
5-th percentile0.069128322
Q10.091633598
median0.10528368
Q30.13251266
95-th percentile0.18008356
Maximum0.21338211
Range0.16242855
Interquartile range (IQR)0.040879057

Descriptive statistics

Standard deviation0.036044786
Coefficient of variation (CV)0.31892144
Kurtosis1.5746515
Mean0.11302089
Median Absolute Deviation (MAD)0.021433109
Skewness1.012327
Sum3.0515641
Variance0.0012992266
MonotonicityNot monotonic
2023-04-16T14:10:04.632048image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
0.08153026027 1
 
3.7%
0.1086859688 1
 
3.7%
0.1183513209 1
 
3.7%
0.2133821064 1
 
3.7%
0.1145733167 1
 
3.7%
0.134762634 1
 
3.7%
0.05095355947 1
 
3.7%
0.105283681 1
 
3.7%
0.1224934216 1
 
3.7%
0.07685893121 1
 
3.7%
Other values (17) 17
63.0%
ValueCountFrequency (%)
0.05095355947 1
3.7%
0.06660184087 1
3.7%
0.07502344483 1
3.7%
0.07685893121 1
3.7%
0.08153026027 1
3.7%
0.0838505725 1
3.7%
0.08855029428 1
3.7%
0.0947169017 1
3.7%
0.0956459595 1
3.7%
0.09922161654 1
3.7%
ValueCountFrequency (%)
0.2133821064 1
3.7%
0.1927952023 1
3.7%
0.1504230649 1
3.7%
0.1497037114 1
3.7%
0.1420472245 1
3.7%
0.134762634 1
3.7%
0.1335666912 1
3.7%
0.1314586191 1
3.7%
0.1224934216 1
3.7%
0.1183513209 1
3.7%
Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.27363691
Minimum0.15228035
Maximum0.51821369
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size344.0 B
2023-04-16T14:10:04.722745image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.15228035
5-th percentile0.17342963
Q10.22736582
median0.26597377
Q30.30468156
95-th percentile0.3970437
Maximum0.51821369
Range0.36593334
Interquartile range (IQR)0.077315741

Descriptive statistics

Standard deviation0.079397499
Coefficient of variation (CV)0.29015639
Kurtosis2.2970799
Mean0.27363691
Median Absolute Deviation (MAD)0.0455333
Skewness1.0677725
Sum7.3881966
Variance0.0063039629
MonotonicityNot monotonic
2023-04-16T14:10:04.799250image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
0.2445907808 1
 
3.7%
0.1870824053 1
 
3.7%
0.3440319314 1
 
3.7%
0.5182136869 1
 
3.7%
0.2659737709 1
 
3.7%
0.297856049 1
 
3.7%
0.2620468773 1
 
3.7%
0.2534607136 1
 
3.7%
0.2359132565 1
 
3.7%
0.2923653462 1
 
3.7%
Other values (17) 17
63.0%
ValueCountFrequency (%)
0.1522803501 1
3.7%
0.1718327495 1
3.7%
0.1771556865 1
3.7%
0.1793906824 1
3.7%
0.1870824053 1
3.7%
0.1993568693 1
3.7%
0.2188183807 1
3.7%
0.2359132565 1
3.7%
0.2445907808 1
3.7%
0.2471250306 1
3.7%
ValueCountFrequency (%)
0.5182136869 1
3.7%
0.4145173176 1
3.7%
0.3562719098 1
3.7%
0.3440319314 1
3.7%
0.3365712338 1
3.7%
0.3201996049 1
3.7%
0.3115070712 1
3.7%
0.297856049 1
3.7%
0.2937050315 1
3.7%
0.2923653462 1
3.7%

Outras malformações congênitasprop
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.12249519
Minimum0.063937767
Maximum0.19497666
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size344.0 B
2023-04-16T14:10:04.894377image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.063937767
5-th percentile0.069407636
Q10.10243132
median0.12383783
Q30.14177794
95-th percentile0.1802633
Maximum0.19497666
Range0.1310389
Interquartile range (IQR)0.039346619

Descriptive statistics

Standard deviation0.033559539
Coefficient of variation (CV)0.27396618
Kurtosis-0.21486768
Mean0.12249519
Median Absolute Deviation (MAD)0.021743769
Skewness0.23016182
Sum3.3073701
Variance0.0011262426
MonotonicityNot monotonic
2023-04-16T14:10:04.990274image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
0.1379742866 1
 
3.7%
0.1086859688 1
 
3.7%
0.1808697286 1
 
3.7%
0.1493674745 1
 
3.7%
0.1237801011 1
 
3.7%
0.1355283308 1
 
3.7%
0.1455815985 1
 
3.7%
0.1364788458 1
 
3.7%
0.1247618183 1
 
3.7%
0.1125254941 1
 
3.7%
Other values (17) 17
63.0%
ValueCountFrequency (%)
0.06393776724 1
3.7%
0.06457457882 1
3.7%
0.08068476812 1
3.7%
0.08524794677 1
3.7%
0.08626020046 1
3.7%
0.08909696179 1
3.7%
0.09831297663 1
3.7%
0.1065496709 1
3.7%
0.1080047071 1
3.7%
0.1084044608 1
3.7%
ValueCountFrequency (%)
0.1949766642 1
3.7%
0.1808697286 1
3.7%
0.1788483153 1
3.7%
0.1500468897 1
3.7%
0.1493674745 1
3.7%
0.1491138378 1
3.7%
0.1455815985 1
3.7%
0.1379742866 1
3.7%
0.1366862022 1
3.7%
0.1364788458 1
3.7%

Anomalias cromossômicas não classificadas em outra parteprop
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.046401936
Minimum0.017041581
Maximum0.097182724
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size344.0 B
2023-04-16T14:10:05.080977image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.017041581
5-th percentile0.019011112
Q10.02877465
median0.040342384
Q30.060664766
95-th percentile0.091569218
Maximum0.097182724
Range0.080141143
Interquartile range (IQR)0.031890116

Descriptive statistics

Standard deviation0.022406878
Coefficient of variation (CV)0.4828867
Kurtosis0.048579202
Mean0.046401936
Median Absolute Deviation (MAD)0.012907542
Skewness0.82388587
Sum1.2528523
Variance0.00050206817
MonotonicityNot monotonic
2023-04-16T14:10:05.168984image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
0.05017246786 1
 
3.7%
0.03563474388 1
 
3.7%
0.0963072581 1
 
3.7%
0.02743484225 1
 
3.7%
0.09718272398 1
 
3.7%
0.06967840735 1
 
3.7%
0.03639539962 1
 
3.7%
0.01949697797 1
 
3.7%
0.05217312404 1
 
3.7%
0.03767594667 1
 
3.7%
Other values (17) 17
63.0%
ValueCountFrequency (%)
0.01704158146 1
3.7%
0.01880288311 1
3.7%
0.01949697797 1
3.7%
0.02202104233 1
3.7%
0.02505879178 1
3.7%
0.02743484225 1
3.7%
0.02824449042 1
3.7%
0.02930480998 1
3.7%
0.03563474388 1
3.7%
0.03639539962 1
3.7%
ValueCountFrequency (%)
0.09718272398 1
3.7%
0.0963072581 1
3.7%
0.08051379033 1
3.7%
0.06967840735 1
3.7%
0.06877149109 1
3.7%
0.06668190825 1
3.7%
0.06554198442 1
3.7%
0.05578754789 1
3.7%
0.05217312404 1
3.7%
0.05017246786 1
3.7%

Interactions

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2023-04-16T14:09:55.302074image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:09:56.602831image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:09:57.998923image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:09:59.329915image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:10:00.607063image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Correlations

2023-04-16T14:10:05.267124image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Casos_entre_nascidosEspinha bífidapropOutras malformações congênitas do sistema nervosopropMalformações congênitas do aparelho circulatóriopropFenda labial e fenda palatinapropAusência atresia e estenose do intestino delgadopropOutras malformações congênitas do aparelho digestivopropTesticulo não-descidopropOutras malformações do aparelho geniturináriopropDeformidades congênitas do quadrilpropDeformidades congênitas dos péspropOutras malformações e deformidades congênitas do aparelho osteomuscularpropOutras malformações congênitaspropAnomalias cromossômicas não classificadas em outra partepropUFEstado
Casos_entre_nascidos1.0000.5810.3390.5120.5350.0400.6690.4580.7100.0660.6050.7280.7080.5101.0001.000
Espinha bífidaprop0.5811.0000.2800.5250.451-0.0140.4430.4590.623-0.2100.4730.4740.3950.2721.0001.000
Outras malformações congênitas do sistema nervosoprop0.3390.2801.0000.165-0.180-0.1950.339-0.1110.0980.1530.1040.0540.1560.2131.0001.000
Malformações congênitas do aparelho circulatórioprop0.5120.5250.1651.0000.4330.5070.3960.4120.6310.2490.4830.5130.2440.5101.0001.000
Fenda labial e fenda palatinaprop0.5350.451-0.1800.4331.0000.2170.4300.6260.646-0.0750.6560.5070.4990.1171.0001.000
Ausência atresia e estenose do intestino delgadoprop0.040-0.014-0.1950.5070.2171.0000.0310.2040.1050.1980.0520.2090.0340.3041.0001.000
Outras malformações congênitas do aparelho digestivoprop0.6690.4430.3390.3960.4300.0311.0000.1680.394-0.0220.4440.2720.3440.4211.0001.000
Testiculo não-descidoprop0.4580.459-0.1110.4120.6260.2040.1681.0000.600-0.0080.5470.6210.4390.0851.0001.000
Outras malformações do aparelho geniturinárioprop0.7100.6230.0980.6310.6460.1050.3940.6001.0000.0750.6470.7560.5240.3721.0001.000
Deformidades congênitas do quadrilprop0.066-0.2100.1530.249-0.0750.198-0.022-0.0080.0751.0000.2680.2910.061-0.0911.0001.000
Deformidades congênitas dos pésprop0.6050.4730.1040.4830.6560.0520.4440.5470.6470.2681.0000.6370.2560.0891.0001.000
Outras malformações e deformidades congênitas do aparelho osteomuscularprop0.7280.4740.0540.5130.5070.2090.2720.6210.7560.2910.6371.0000.5460.1851.0001.000
Outras malformações congênitasprop0.7080.3950.1560.2440.4990.0340.3440.4390.5240.0610.2560.5461.0000.2191.0001.000
Anomalias cromossômicas não classificadas em outra parteprop0.5100.2720.2130.5100.1170.3040.4210.0850.372-0.0910.0890.1850.2191.0001.0001.000
UF1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
Estado1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000

Missing values

2023-04-16T14:10:02.167608image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-04-16T14:10:02.399769image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

UFEstadoCasos_entre_nascidosEspinha bífidapropOutras malformações congênitas do sistema nervosopropMalformações congênitas do aparelho circulatóriopropFenda labial e fenda palatinapropAusência atresia e estenose do intestino delgadopropOutras malformações congênitas do aparelho digestivopropTesticulo não-descidopropOutras malformações do aparelho geniturináriopropDeformidades congênitas do quadrilpropDeformidades congênitas dos péspropOutras malformações e deformidades congênitas do aparelho osteomuscularpropOutras malformações congênitaspropAnomalias cromossômicas não classificadas em outra parteprop
0ACAcre0.0064600.0125430.0250860.0062720.0564440.0062720.0250860.0125430.0501720.0000000.0815300.2445910.1379740.050172
1ALAlagoas0.0077140.0166340.0706930.0311880.0644560.0020790.0395050.0103960.0852480.0062380.1497040.3202000.0852480.039505
2AMAmazonas0.0047420.0106560.0519490.0266410.0546140.0013320.0306370.0173160.0213130.0000000.0666020.1718330.0639380.029305
3APAmapá0.0177560.0187560.0937790.0125040.0562680.0000001.1503590.0000000.0375120.0000000.0750230.2188180.1500470.068771
4BABahia0.0073510.0128960.0639430.0596440.0429870.0016120.0392260.0091350.0741520.0042990.0956460.2890870.1080050.042450
5CECeará0.0102300.0352770.0959870.0771180.0631710.0016410.0451220.0311750.0828610.0065630.1927950.2937050.1788480.055788
6DFDistrito Federal0.0086110.0171470.0476300.0571560.0609660.0019050.0533460.0171470.0819230.0095260.1314590.3562720.1238380.066682
7ESEspírito Santo0.0084630.0411930.1067280.0992380.0655340.0037450.0655340.0112340.0992380.0018720.1048550.2864790.1366860.080514
8GOGoiás0.0077000.0323970.0697780.0398730.0672860.0012460.0373810.0199360.0785000.0012460.1420470.3115070.1084040.043611
9MAMaranhão0.0047130.0240950.0559000.0163850.0366240.0009640.0289140.0000000.0395160.0028910.0838510.1522800.0645750.025059
UFEstadoCasos_entre_nascidosEspinha bífidapropOutras malformações congênitas do sistema nervosopropMalformações congênitas do aparelho circulatóriopropFenda labial e fenda palatinapropAusência atresia e estenose do intestino delgadopropOutras malformações congênitas do aparelho digestivopropTesticulo não-descidopropOutras malformações do aparelho geniturináriopropDeformidades congênitas do quadrilpropDeformidades congênitas dos péspropOutras malformações e deformidades congênitas do aparelho osteomuscularpropOutras malformações congênitaspropAnomalias cromossômicas não classificadas em outra parteprop
17PRParaná0.0066570.0191160.0587150.0914860.0662250.0034140.0355020.0102410.0621280.0000000.1051400.1993570.0983130.065542
18RJRio de Janeiro0.0070630.0231080.0648030.0602820.0442060.0020090.0271270.0221030.0683190.0015070.0768590.2923650.1125250.037676
19RNRio Grande do Norte0.0078490.0226840.0884670.0657840.0612470.0000000.0476360.0068050.1066150.0022680.1224930.2359130.1247620.052173
20RORondônia0.0070970.0389940.0584910.0389940.0818870.0000000.0506920.0194970.0779880.0000000.1052840.2534610.1364790.019497
21RRRoraima0.0069880.0145580.0946280.0291160.0509540.0000000.0145580.0000000.0582330.0072790.0509540.2620470.1455820.036395
22RSRio Grande do Sul0.0096940.0229710.0428790.1347630.0865240.0022970.0474730.0199080.1056660.0030630.1347630.2978560.1355280.069678
23SCSanta Catarina0.0089200.0255740.0624020.1155960.0757000.0040920.0480800.0245510.0859300.0010230.1145730.2659740.1237800.097183
24SESergipe0.0111260.0396280.0640150.0396280.0823050.0000000.0640150.0457250.1432710.0000000.2133820.5182140.1493670.027435
25SPSão Paulo0.0129540.0298140.0829360.2984980.0731790.0075890.1194350.0336080.1308190.0160810.1183510.3440320.1808700.096307
26TOTocantins0.0075840.0170420.0511250.0639060.0809480.0042600.0298230.0213020.0852080.0170420.1107700.3365710.1491140.017042